lindelani-3/covidtrends

COVID-19 Trend Analysis

This project aims to provide a comprehensive analysis of the COVID-19 pandemic's impact across various regions of the world. Utilizing data up to 2021, our analysis focuses on identifying trends in confirmed cases, deaths, recoveries, and other critical metrics across different WHO regions. Through data preparation, cleaning, manipulation, and visualization, we have developed insights into how the pandemic has evolved and affected populations globally.

Tools

MS Excel, MS Power BI, MySQL

Data

Our dataset encompasses daily updated COVID-19 statistics, including the number of confirmed cases, deaths, recoveries, and active cases. Data is segmented by country/region and includes geographical coordinates (latitude and longitude), facilitating regional analysis. This dataset was carefully prepared and cleaned using Microsoft Excel, ensuring accuracy and reliability for subsequent analyses.

MySQL Database

Following initial data preparation, we performed further data manipulation using SQL to enhance our dataset with additional metrics such as new daily cases and case fatality rates. This enriched dataset allows for a more nuanced understanding of the pandemic's dynamics, including the rate at which it spreads and the lethality of the virus across different regions and periods.

Dashboard

The Power BI dashboard PDF provides a comprehensive visualization of COVID-19 trends across different WHO regions, covering metrics such as average confirmed cases, new cases, deaths, active cases, and recoveries. It segments data by WHO regions including the Western Pacific, Europe, Americas, South-East Asia, Africa, and the Eastern Mediterranean, offering detailed insights into the pandemic's impact across these areas. The dashboard also highlights specific countries within these regions, allowing for a closer examination of COVID-19 trends and outcomes.

Our analysis revealed significant differences in the impact of COVID-19 across WHO regions, with varying trends in case numbers, fatalities, and recovery rates. Through our dashboard, stakeholders can explore these trends in detail, gaining valuable insights into the pandemic's evolution and identifying areas that may require more focused public health interventions.

Conclusion

While Excel and SQL provided robust tools for our initial analysis, we recognize the potential benefits of incorporating Python for future work. Python's advanced data manipulation, analysis capabilities, and machine learning algorithms could offer deeper insights, improve forecasting models, and identify patterns that may not be immediately apparent from traditional analysis methods.

The COVID-19 Trend Analysis Project highlights the power of data analysis and visualization in understanding complex global health emergencies. Our work provides a foundation for further research, policy-making, and public health strategy development, aiming to mitigate the pandemic's impact and prepare for future health crises.